Abstract
Due to the increase of the renewable energy and variation of the distributed sources, the power system experience more variability and uncertainty than before. In this paper, we propose an framework for steady-state analysis of power systems using the joint probabilistic information about the wind speeds and loads. Vine copula is employed to capture the the complex dependencies between wind speeds and loads in the multiple regions. In addition, we investigate probabilistic modeling based on the parametric estimation and Wasserstein distance to depict the marginal distribution of wind speed and load. The effectiveness of the proposed method was tested on an IEEE 39-bus system using Python and MATLAB. From the simulation, it was found that the probabilistic power flow method (PPF) that combined vine copula sampling outperforms the deterministic approach and the PPF with random simple sampling in terms of the quantitative risk assessment in the power system.
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